Accurate recognition of the traffic condition can proactively alert drivers who will enter the congested road to avoid congestion, so that the degree of congestion will not be increased. And it is also the basis to make scientific decision on active traffic managements, and conducive to alleviate congestion, improve the traffic efficiency, save energy and reduce emission. In this paper, the traffic surveillance videos are sampled every three minutes to build static image database, and the road area is marked as the region of interest (ROI), and then ROI images are normalized in terms of angle and scale. The three image features in ROI, i.e., average gradient, corner and long edge number, are then extracted. Finally, the fuzzy C-means clustering (FCM) method is used to classify the traffic condition into two classifications, i.e., flowing traffic and congestion. Experimental results show that the proposed algorithm can effectively identify the traffic condition involved in the image by the accuracy of 98％. Moreover, compared with the video-based approaches, this method greatly reduces the implementation cost.
